It is good to study the statistics and fundamentals of the methods and technologies of data mining, and most importantly, to have a great desire to learn and find patterns in the data arrays, taking into account the objectives of the research.

Learning the fundamentals of data mining techniques is the best way to master the field of data mining.

K.H. Leung Tank You

data mining is an intersection of several disciplines like; statistic, database, ML, operation optimization and parallel computing.

there are various definition for data mining

"technique" for knowledge discovery, "process" for pattern recognition, etc.

in my opinions, learning data mining techniques are not a nightmare, but how to use these techniques and how to chose strategy is more important. specially in deep learning that architecture of neuron network is a success factor to get best result. in a simple word i can say, practice and experience is crucial to be a real expert or specialist.

as i am student, it's my personal idea and can be wrong.

Muhammad Ehsan Thanks for your valuable comment

It is good to study the statistics and fundamentals of the methods and technologies of data mining, and most importantly, to have a great desire to learn and find patterns in the data arrays, taking into account the objectives of the research.

I would agree with your respondents so far. The technical skills required for data mining centre around knowledge of statistical techniques (such as linear and log regressions, Principal Component Analysis, clustering etc), statistical packages (such as SPSS, Stata, SAS etc), ability to use programming languages (Python, R etc) and Machine Learning techniques and algorithms (data cleaning, kNN, Decision Trees, Support Vector Machines and Artificial Neural Networks). Luckily, there are plenty of introductory courses online to allow you to map the scope (and scale) of the area.

I would add that to become 'good' at data mining (let alone become an expert) some softer skills are needed such as good team working (because not everyone can be good at everything), ability to ask useful questions and a curiosity to find out something that no-one else knows. Being able to present your results effectively through explanations and visualisation techniques are also skill to be valued highly.

Firstly though, you might want to ask yourself: do you want to become an expert in data mining itself or an expert in DM techniques for use in your own field? These are two valid objectives but I think that they require different approaches and levels of competency. Hope this helps

You should learn about a) Data processing (Structured, Semi-Structured and unstructured data), b) Statistic's Fundamentals, c) and carefully to choose the field in which you want to analyze data for understanding the underlying business.

Once the steps are finished, you can advance on the exploratory data analysis and the fundamental problems of data (Missing Values, Outliers, Consistency, etc).

Follows, the basic unsupervised and supervised techniques could be introduced for initial applications.

Mario José Diván Vladimir G. Krasilenko John Bohan Thanks for your valuable comments

Studying algorithm, statistics and information theory